Belgian Mosquito Virome
Lander De Coninck
R script to analyse the data on virome sequencing of single Belgian mosquitoes.
necessary_packages <- c('tidyverse', 'data.table', 'scales', 'knitr', 'grid', 'magrittr', 'DT', 'here', 'gridtext',
'ggpubr', 'ggthemes', 'reshape2', 'viridis', 'pals', 'circlize', 'scatterpie', 'ggh4x',
'vegan', 'phyloseq', 'metagenomeSeq', 'ComplexHeatmap', 'decontam')
lapply(necessary_packages, library, character.only = TRUE)
i_am("BelgianMosquitoVirome.R")
setwd(here::here())
source("BMV_functions.R")## [1] "R version 4.1.3 (2022-03-10)"## [1] "Running under: macOS Big Sur/Monterey 10.16"## [1] "Biobase 2.52.0" "BiocGenerics 0.38.0" "circlize 0.4.15"
## [4] "ComplexHeatmap 2.8.0" "data.table 1.14.4" "decontam 1.12.0"
## [7] "dplyr 1.1.0" "DT 0.26" "forcats 1.0.0"
## [10] "ggh4x 0.2.4" "ggplot2 3.4.2" "ggpubr 0.6.0"
## [13] "ggthemes 4.2.4" "glmnet 4.1-4" "gridtext 0.1.5"
## [16] "here 1.0.1" "knitr 1.40" "lattice 0.20-45"
## [19] "limma 3.48.3" "lubridate 1.9.2" "magrittr 2.0.3"
## [22] "Matrix 1.4-0" "metagenomeSeq 1.34.0" "pals 1.7"
## [25] "permute 0.9-7" "phyloseq 1.36.0" "purrr 1.0.2"
## [28] "RColorBrewer 1.1-3" "readr 2.1.4" "reshape2 1.4.4"
## [31] "scales 1.2.1" "scatterpie 0.1.8" "stringr 1.5.0"
## [34] "tibble 3.2.1" "tidyr 1.3.0" "tidyverse 2.0.0"
## [37] "vegan 2.6-4" "viridis 0.6.2" "viridisLite 0.4.2"1 Eukaryotic virome analysis
1.1 General sequencing info
seqnum_raw <- read.delim("data/sequencing-info.tsv", sep="\t", header=T)
seqnum_raw_summarized <- seqnum_raw %>% group_by(sample) %>%
summarise(sumseqs = sum(num_seqs))
seqnum <- read.delim("data/sequencing-info-hostout.tsv", sep="\t", header=T)
seqnum_summarized <-seqnum %>% group_by(sample) %>%
summarise(sumseqs_nonhost = sum(num_seqs))
seqnum_final <- merge(seqnum_raw_summarized, seqnum_summarized)
seqnum_final <- seqnum_final %>%
mutate(proportion_nonhost=sumseqs_nonhost/sumseqs*100)
datatable(seqnum_final)## [1] 5294850## [1] 166768## [1] 1386043## [1] 84.91982## [1] 3.138335## [1] 25.64182## [1] 1069326190## [1] 2730503951.2 Prepare the data
1.2.1 Load the OTU table, taxonomy file and metadata into R
OTU <- read.table("data/abundance-table.txt", header=TRUE, row.names=1, sep="\t", dec=".")
names(OTU) <- gsub(x = names(OTU), pattern = "\\.", replacement = "-")
summary(rowSums(OTU))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 33 69 1488 235 4549919## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2522 107065 214236 468777 649110 39950081.2.2 Make a phyloseq object
OTU.UF <- otu_table(as.matrix(OTU), taxa_are_rows=T)
tax.UF <- tax_table(as.matrix(tax))
meta.UF <- sample_data(meta)
BMV <- phyloseq(OTU.UF, tax.UF, meta.UF)
BMV## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 4040 taxa and 207 samples ]
## sample_data() Sample Data: [ 207 samples by 12 sample variables ]
## tax_table() Taxonomy Table: [ 4040 taxa by 7 taxonomic ranks ]1.2.3 Remove contamination
1.2.3.1 Visualize library sizes of samples and negative controls
decontam <- as.data.frame(sample_data(BMV))
decontam$LibrarySize <- sample_sums(BMV)
decontam <- decontam[order(decontam$LibrarySize),]
decontam$Index <- seq(nrow(decontam))
ggplot(data=decontam, aes(x=Index, y=LibrarySize, color=Control)) +
geom_point()+
ggtitle("Library sizes")
1.2.3.2 Detect contaminants
sample_data(BMV)$is.neg <- sample_data(BMV)$Control == "Yes"
contamdf.prev <- isContaminant(BMV, method="prevalence", neg="is.neg")Number of contaminating contigs:
##
## FALSE TRUE
## 3406 6341.2.3.3 Visualize prevalence of contaminants in samples and negative controls
ps.pa <- transform_sample_counts(BMV, function(abund) 1*(abund>0))
ps.pa.neg <- prune_samples(sample_data(ps.pa)$Control == "Yes", ps.pa)
ps.pa.pos <- prune_samples(sample_data(ps.pa)$Control == "No", ps.pa)
df.pa <- data.frame(pa.pos=taxa_sums(ps.pa.pos), pa.neg=taxa_sums(ps.pa.neg),
contaminant=contamdf.prev$contaminant)
ggplot(data=df.pa, aes(x=pa.neg, y=pa.pos, color=contaminant)) + geom_point() +
xlab("Prevalence (Negative Controls)") + ylab("Prevalence (True Samples)")+
ggtitle("Prevalence of contaminants in samples vs NCs")
1.2.3.4 Remove negative controls and contaminants from phyloseq object
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 3406 taxa and 207 samples ]
## sample_data() Sample Data: [ 207 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 3406 taxa by 7 taxonomic ranks ]ps.noncontam <- prune_samples(sample_data(BMV)$Control!='Yes', ps.noncontam)
BMV <- ps.noncontam
BMV## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 3406 taxa and 197 samples ]
## sample_data() Sample Data: [ 197 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 3406 taxa by 7 taxonomic ranks ]Remove negative controls from metadata table
1.2.4 Subset virome data
1.2.4.1 Subset only viruses and remove prokaryotic viruses and EVEs
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 214 taxa and 197 samples ]
## sample_data() Sample Data: [ 197 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 214 taxa by 7 taxonomic ranks ]EVE_phage <- c("Atrato Retro-like virus", "Gurupi chuvirus-like 1", "Aedes aegypti To virus 1",
"Aedes aegypti To virus 2", "Guato virus", "Kaiowa virus", "Atrato Chu-like virus 1",
"Chuvirus Mos8Chu0", "Chibugado virus", "Prokaryotic dsDNA virus sp.")
BMV.V2 <- subset_taxa(BMV.V, !is.element(Species, EVE_phage))
BMV.V2 <- subset_taxa(BMV.V2, Order!="Caudovirales")
BMV.V2 <- subset_taxa(BMV.V2, Phylum!="Phage")
BMV.V2## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 147 taxa and 197 samples ]
## sample_data() Sample Data: [ 197 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 147 taxa by 7 taxonomic ranks ]Info on sample variables and level of taxonomic ranks:
## [1] "Sample" "PCR_Subspecies" "SKA_Subspecies" "Species"
## [5] "Sex" "Location" "Control" "visual_genus"
## [9] "visual_species" "lat" "long" "Municipality"
## [13] "is.neg"## [1] "Kingdom" "Phylum" "Class" "Order" "Family" "Genus" "Species"1.2.4.2 Agglomerate taxa on viral species level
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 47 taxa and 197 samples ]
## sample_data() Sample Data: [ 197 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 47 taxa by 7 taxonomic ranks ]vcount <- as.data.frame(colSums(otu_table(BMV_species) != 0))
names(vcount) <- "viral_count"
vcount %>%
group_by(viral_count) %>%
summarise(n = n()) %>%
ggplot(aes(x=viral_count, y=n))+
geom_col()+
theme_bw()
df <- merge(meta, vcount, by=0) %>%
group_by(viral_count, SKA_Subspecies) %>%
summarise(n = n())
p <- ggplot(df, aes(x=viral_count, y=n, label=n, fill=SKA_Subspecies))+
geom_col()+
scale_x_continuous(n.breaks = max(df$viral_count), name = "# viral species")+
scale_y_continuous(breaks=seq(0, 125, 25), name = "# mosquitoes")+
scale_fill_viridis_d(begin=0.4, end=0.9, name="")+
#geom_text(position =position_stack(vjust=.5), size=3)+
theme_bw()+
theme(legend.text = element_text(face="italic"),
#panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p
Only keep taxa with more than 0 reads:
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 45 taxa and 197 samples ]
## sample_data() Sample Data: [ 197 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 45 taxa by 7 taxonomic ranks ]Only keep samples with more than 0 viral reads:
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 45 taxa and 82 samples ]
## sample_data() Sample Data: [ 82 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 45 taxa by 7 taxonomic ranks ]1.2.5 Prepare relative abundance data
Agglomerate taxa on viral Family level:
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 26 taxa and 82 samples ]
## sample_data() Sample Data: [ 82 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 26 taxa by 7 taxonomic ranks ]Calculate relative abundance of viral families:
BMV_family_re.abund <- BMV_family %>%
transform_sample_counts(function(x) {round(100*(x/sum(x)),5)} )
BMV_family_re.abund## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 26 taxa and 82 samples ]
## sample_data() Sample Data: [ 82 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 26 taxa by 7 taxonomic ranks ]Count mosquito species per location:
sp_count <- psmelt(BMV_family_re.abund) %>%
select(Sample,SKA_Subspecies,Municipality) %>%
distinct() %>%
group_by(Municipality) %>%
count(SKA_Subspecies) %>%
pivot_wider(names_from=Municipality, values_from = n) %>%
replace(is.na(.), 0) %>%
rename("Villers_Le_Bouillet" = `Villers-Le-Bouillet`)Create list of locations:
loc_order <- c("Natoye", "Vrasene", "Villers_Le_Bouillet",
"Frameries", "Maasmechelen", "Bertem",
"Leuven", "Eupen")Create list of barplots with number of mosquitoes per location:
plist <- list(ggplot()+theme_void()+#ggtitle("Mosquito species")+
theme(title = element_text(size = 6)))
for (i in loc_order){
plist[[i]] <- ggplot(sp_count, aes_string(x = "SKA_Subspecies", y=i, fill="SKA_Subspecies"))+
geom_col()+
#scale_color_viridis_d(begin=0.4, end=0.9)+
scale_fill_viridis_d(begin=0.4, end=0.9, name="")+
theme_classic()+
theme(legend.position = "none",
axis.ticks.x=element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
plot.title = element_text(hjust = 0.5, size=10),
panel.background = element_rect(fill = "transparent"),
plot.background = element_rect(fill = "transparent", color = NA),
axis.line = element_line(colour = 'black', size = 0.25),
axis.ticks = element_line(colour = "black", size = 0.25))+
#ggtitle(gsub("_","-",i))+
scale_y_continuous(limits = c(0,20), expand = c(0,0), position = "right")
}
plist[10] <- list(ggplot()+theme_void())
ggarrange(plotlist = plist[2:9], labels = gsub("_","-",loc_order),
font.label = list(size=10, face="plain"), hjust = 0, vjust = 1)
Create colorpalette for viral families:
BMV_smelt <- psmelt(BMV_final)
FamLevel <- levels(as.factor(BMV_smelt[(BMV_smelt$Family!="unclassified"),]$Family))
FamLevel <- c(FamLevel, "unclassified")
BMV_smelt$Family <- factor(BMV_smelt$Family, levels = FamLevel)
myFamCol <- c(stepped(n=20), "#E6550D", "#FD8D3C", "#FDAE6B","lightgrey")
names(myFamCol) <- levels(as.factor(BMV_smelt$Family))
Create colors for each mosquito species:
myColors <- viridis::viridis(4,1, begin=0.4, end = 0.9, direction = 1)
names(myColors) <- levels(as.factor(BMV_smelt$SKA_Subspecies))
1.2.6 Prepare heatmap data
Make table with fungi reads:
BMV_fungi <- subset_taxa(BMV, Phylum %in% c("Ascomycota", "Basidiomycota",
"Chytridiomycota", "Glomeromycota",
"Zygomycota","Neocallimastigomycota",
"Microsporidia"))
BMV_fungi## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 112 taxa and 197 samples ]
## sample_data() Sample Data: [ 197 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 112 taxa by 7 taxonomic ranks ]## [1] "Sample" "PCR_Subspecies" "SKA_Subspecies" "Species"
## [5] "Sex" "Location" "Control" "visual_genus"
## [9] "visual_species" "lat" "long" "Municipality"
## [13] "is.neg"fungi <- as.data.frame(otu_table(BMV_fungi))
fungi2 <- fungi %>%
mutate(Phylum=rownames(.), .before=1) %>%
pivot_longer(cols = 2:198, names_to="Sample", values_to = "fungi_reads") %>%
pivot_wider(names_from = Phylum, values_from = fungi_reads)
fungi2## Taxonomy Table: [4 taxa by 7 taxonomic ranks]:
## Kingdom Phylum
## NODE_31_length_1205_cov_6.800532_NEMO53|full "Eukaryota" "Ascomycota"
## NODE_8_length_3532_cov_13.946454_NEMO31|full "Eukaryota" "Chytridiomycota"
## NODE_58_length_1305_cov_1096.565961_NEMO36|full "Eukaryota" "Microsporidia"
## NODE_129_length_1223_cov_14.773124_MEMO055|full "Eukaryota" "Basidiomycota"
## Class Order Family Genus
## NODE_31_length_1205_cov_6.800532_NEMO53|full NA NA NA NA
## NODE_8_length_3532_cov_13.946454_NEMO31|full NA NA NA NA
## NODE_58_length_1305_cov_1096.565961_NEMO36|full NA NA NA NA
## NODE_129_length_1223_cov_14.773124_MEMO055|full NA NA NA NA
## Species
## NODE_31_length_1205_cov_6.800532_NEMO53|full NA
## NODE_8_length_3532_cov_13.946454_NEMO31|full NA
## NODE_58_length_1305_cov_1096.565961_NEMO36|full NA
## NODE_129_length_1223_cov_14.773124_MEMO055|full NAConvert phyloseq object to metagenomeseq object to make heatmap:
Aggregate by species:
BMV_metaseq_species <- aggregateByTaxonomy(BMV_metaseq, lvl = "Species", norm = F,
aggfun = colSums, out = "MRexperiment", alternate = T)Count number of unique viral species:
Assign mosquito species for each sample:
Assign colors to location:
location <- pData(BMV_metaseq_species)$Municipality
unique_locations <- length(unique(pData(BMV_metaseq_species)$Municipality))
locColors <- viridis::plasma(unique_locations, 1, begin=0, end = 0.9, direction = 1)
names(locColors) <- levels(as.factor(pData(BMV_metaseq_species)$Municipality))
Set heatmap colors:
Calculate average BLASTx values:
#blastx <- read.table("data/BEmosquitoes.tsv", header=T, row.names=1, dec=".", sep="\t")
blastx <- read.table("data/BMV_2023.pid.tsv", header=T, row.names=1, dec=".", sep="\t")
blastx <- dplyr::select(blastx, 2)
blastx.UF <- phyloseq::otu_table(as.matrix(blastx), taxa_are_rows=T)
blastx.ps <- phyloseq(blastx.UF, tax_table(prune_taxa(taxa_sums(BMV.V2) >= 1, BMV.V2)))
blastx_metaseq <- phyloseq_to_metagenomeSeq(blastx.ps)
blastx_metaseq## MRexperiment (storageMode: environment)
## assayData: 141 features, 1 samples
## element names: counts
## protocolData: none
## phenoData: none
## featureData
## featureNames: NODE_101_length_1285_cov_17.030629_MEMO115|full
## NODE_106_length_1164_cov_11.001840_MEMO031|full ...
## NODE_9_length_5477_cov_48.040926_MEMO031|full (141 total)
## fvarLabels: OTUname Kingdom ... Species (8 total)
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation:#Aggregate by species
blastx_mean <- aggregateByTaxonomy(blastx_metaseq, lvl = "Species", norm = F, aggfun = mean, out = "MRexperiment", alternate = T)
blastx_mean## MRexperiment (storageMode: environment)
## assayData: 45 features, 1 samples
## element names: counts
## protocolData: none
## phenoData: none
## featureData
## featureNames: Aedes aegypti anphevirus Aedes alboannulatus
## orthomyxo-like virus ... Yongsan picorna-like virus 2 (45 total)
## fvarLabels: OTUname Kingdom ... Species (8 total)
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation:Store average BLASTx identities in dataframe:
rowanno <- as.data.frame(returnAppropriateObj(blastx_mean, log=F, norm=F))
colnames(rowanno)[1] <- "blastx"##
## FALSE TRUE
## 27 18Create color function for BLASTx values:
Get Baltimore classification/genome composition for each viral species from ICTV:
taxV <- as.data.frame(tax_table(BMV_final))
baltimore <- read.table("data/ICTV_classification_family.tsv", header=T, sep="\t")
balt_gc <- baltimore %>%
select(Family, Genome.Composition) %>%
distinct() %>%
add_row(Family="Picorna-like", Genome.Composition="ssRNA(+)") %>%
add_row(Family="Orthomyxo-like", Genome.Composition="ssRNA(-)") %>%
add_row(Family="Negeviridae", Genome.Composition="ssRNA(+)") %>%
add_row(Family="Luteoviridae", Genome.Composition="ssRNA(+)")
tax_gc <- left_join(taxV, balt_gc, by="Family") %>%
filter(!Genome.Composition %in% c("ssRNA(+/-)", "ssDNA(+/-)", "ssDNA(+)", "ssRNA")) %>% # remove double genome compositions
mutate(Genome.Composition = case_when(Species == 'Sclerotinia sclerotiorum dsRNA virus 3' ~ 'dsRNA',
Family == 'Reo-like'~'dsRNA',
Species == 'unclassified' ~'unknown',
Family == 'Totiviridae'~'dsRNA',
Species == 'Hubei orthoptera virus 4' ~ 'ssRNA(+)',
Species == 'Hubei odonate virus 15' ~ 'dsRNA',
Species == 'Wuhan insect virus 26' ~ 'dsRNA',
Species == 'Wuhan insect virus 13' ~ 'ssRNA(+)',
Species == "Linepithema humile qinvirus-like virus 1" ~'ssRNA(-)',
Species =="Hubei virga-like virus 23" ~ 'ssRNA(+)',
Species == "Culex mononega like virus 2" ~'ssRNA(-)',
TRUE ~ `Genome.Composition`))
gcanno<-tax_gc %>% select(Genome.Composition, Family)
rownames(gcanno)<-tax_gc$Species
colnames(gcanno) <- c('GC', 'Family')
gcanno<-gcanno[order(rownames(gcanno)) , ,drop=F]
gcanno$Family <- factor(gcanno$Family, levels = FamLevel)1.3 Alpha diversity
df <- as_tibble(sample_data(BMV_final))
df$LibrarySize <- sample_sums(BMV_final)
df<- df[order(df$LibrarySize),]
threshold <- quantile(df$LibrarySize, 0.05)
min_depth <- df %>%
dplyr::filter(LibrarySize >= 100) %>%
select(LibrarySize) %>%
min()
ggplot(data=df, aes(x=LibrarySize, y="samples", color=SKA_Subspecies)) +
geom_jitter(height = 0.02)+
ggtitle("Library sizes")+
geom_vline(xintercept = min_depth, linetype="dashed", color="grey")+
labs(y="")+
scale_x_log10()+
scale_color_manual(values = myColors, name="Mosquito species")+
theme_bw()
set.seed(1234)
alpha_df_list <- purrr::map(1:1000, ~alpha_rarefied(ab_table=as_tibble(otu_table(BMV_final)), sequencing_depth=min_depth))
alpha_average_df <- Reduce(`+`, alpha_df_list) / length(alpha_df_list)
alpha_average_df %<>%
mutate(Observed=round(Observed))
labels <- c(expression(paste(italic("Aedes japonicus"), " (n=8)")),
expression(paste(italic("Culex pipiens molestus"), " (n=23)")),
expression(paste(italic("Culex pipiens pipiens"), " (n=48)")),
expression(paste(italic("Culex torrentium"), " (n=3)")))
alpha <- alpha_average_df %>%
rownames_to_column("Sample") %>%
left_join(meta %>% select(Sample, SKA_Subspecies)) %>%
pivot_longer(c(-SKA_Subspecies, -Sample), names_to = "Metric", values_to = "Diversity") %>%
ggplot(aes(x=SKA_Subspecies, y=Diversity, color=SKA_Subspecies))+
geom_boxplot(outlier.shape = NA)+
geom_jitter(width=0.2)+
facet_wrap(~Metric, nrow=1, scales="free_y")+
theme_bw()+
scale_color_viridis_d(begin=0.4, end =0.9, name="", labels=labels)+
#scale_fill_viridis_d(begin=0.4, end =0.9, name="", labels=labels)+
theme(strip.text.x = element_text(size = 10),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.text.align = 0)+
scale_y_continuous(limits = c(0, NA))+
stat_pwc(method = "wilcox.test", aes(x=SKA_Subspecies, y=Diversity,
group=SKA_Subspecies), p.adjust.method="BH",
label = "p.adj", hide.ns="p.adj", show.legend = F, tip.length = 0.01)
alpha
# Without rarefaction
#alpha <- plot_richness(BMV_final, measures=c("Observed", "Shannon", "Simpson"), x="SKA_Subspecies", color = "SKA_Subspecies")+
# geom_boxplot()+
# geom_jitter(width=0)+
# theme_bw()+
# scale_color_viridis_d(begin=0.4, end =0.9, name="", labels=c(expression(paste(italic("Aedes japonicus"), " (n=8)")),
# expression(paste(italic("Culex pipiens molestus"), " (n=23)")),
# expression(paste(italic("Culex pipiens pipiens"), " (n=48)")),
# expression(paste(italic("Culex torrentium"), " (n=3)"))))+
# theme(strip.text.x = element_text(size = 10),
# axis.title.x = element_blank(),
# axis.text.x = element_blank(),
# axis.ticks.x = element_blank(),
# legend.text.align = 0)+
# scale_y_continuous(limits = c(0, NA))+
# stat_pwc(method = "wilcox.test", aes_string(x="SKA_Subspecies", y="value",
# group="SKA_Subspecies"), p.adjust.method="BH",
# label = "p.adj", hide.ns="p.adj", show.legend = F, tip.length = 0.01)
#alpha
ggsave("figures/alpha-diversity-species.pdf", alpha, dpi=300)1.4 Ordination
1.4.1 PCoA
# Without rarefaction
#v.ord <- ordinate(BMV_final, method = "PCoA")
#pcoa <- plot_ordination(BMV_final, v.ord, type="samples", color="SKA_Subspecies", shape = "Municipality")+
# scale_shape_manual(values = c(15,16,17,3:8), guide =guide_legend(label.theme = element_text(size=10)), name="Location")+
# scale_color_viridis_d(begin=0, end =1, name="Mosquito species")+
# stat_ellipse(type = "norm", linetype = 2, aes_string(group="SKA_Subspecies"), show.legend = F)+
# theme_bw()+
# theme(panel.grid.minor = element_blank())+
# guides(col = guide_legend(override.aes = list(shape = 15, size = 5),
# label.theme = element_text(size=10, face="italic")))
#
#pcoa
vegan_avgdist <- avgdist(as.data.frame(t(otu_table(BMV_final))),
sample=min_depth, iterations = 1000)
v.ord <- ape::pcoa(vegan_avgdist)
pcoa <- plot_ordination(BMV_final, v.ord, type="samples", color="SKA_Subspecies", shape = "Municipality", axes = c(1, 2))+
scale_shape_manual(values = c(15,16,17,3:8), guide =guide_legend(label.theme = element_text(size=10)), name="Location")+
scale_color_viridis_d(begin=0, end =1, name="Mosquito species")+
stat_ellipse(type = "norm", linetype = 2, aes_string(group="SKA_Subspecies"), show.legend = F)+
theme_bw()+
theme(panel.grid.minor = element_blank())+
guides(col = guide_legend(override.aes = list(shape = 15, size = 5),
label.theme = element_text(size=10, face="italic")))
pcoa
Permanova test
samples_vegan <- row.names(as.matrix(vegan_avgdist))
metadata_vegan <- meta %>%
filter(Sample %in% samples_vegan)
perm <- adonis2(vegan_avgdist ~ SKA_Subspecies+Municipality,
data = metadata_vegan)
permpval<-perm$`Pr(>F)`[1]
Rsq <- perm$R2[1]
pcoa<-pcoa+
ggplot2::annotate(geom="text",x=-.9, y=c(.7,.63,.57), size=c(4,3,3), hjust=0, label =c("Adonis test",
as.expression(bquote(italic(R^2) ~ "=" ~ .(round(Rsq, 2)))),
as.expression(bquote(italic(p) ~ "=" ~ .(pval)))))
pcoa
Beta dispersion
##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 3 2.2094 0.73645 10.783 999 0.001 ***
## Residuals 75 5.1224 0.06830
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 11.4.2 NMDS full dataset
## Run 0 stress 0.004703658
## Run 1 stress 0.002656793
## ... New best solution
## ... Procrustes: rmse 0.1030834 max resid 0.2642104
## Run 2 stress 0.002154896
## ... New best solution
## ... Procrustes: rmse 0.1049748 max resid 0.3274426
## Run 3 stress 0.003353023
## Run 4 stress 0.001464285
## ... New best solution
## ... Procrustes: rmse 0.1097427 max resid 0.4552431
## Run 5 stress 0.002544182
## Run 6 stress 0.002445505
## Run 7 stress 0.00468344
## Run 8 stress 0.002582366
## Run 9 stress 0.003547187
## Run 10 stress 0.003158927
## Run 11 stress 0.002058907
## Run 12 stress 0.003005965
## Run 13 stress 0.004104025
## Run 14 stress 0.002296557
## Run 15 stress 0.001450584
## ... New best solution
## ... Procrustes: rmse 0.1070624 max resid 0.3900057
## Run 16 stress 0.003461795
## Run 17 stress 0.002491895
## Run 18 stress 0.00254531
## Run 19 stress 0.001713396
## ... Procrustes: rmse 0.1086347 max resid 0.3658929
## Run 20 stress 0.00365229
## *** Best solution was not repeated -- monoMDS stopping criteria:
## 20: no. of iterations >= maxitnmds <- plot_ordination(BMV_final, v.ord, type="samples", color="SKA_Subspecies", shape = "Municipality")+
scale_shape_manual(values = c(15,16,17,3:8), guide =guide_legend(label.theme = element_text(size=10)), name="Location")+
scale_color_viridis_d(begin=0, end =1, name="Mosquito species")+
#stat_ellipse(type = "norm", linetype = 2, aes_string(group="SKA_Subspecies"), show.legend = F)+
theme_bw()+
theme(panel.grid.minor = element_blank())+
guides(col = guide_legend(override.aes = list(shape = 15, size = 5),
label.theme = element_text(size=10, face="italic")))
nmds
1.4.3 NMDS singletons removed
BMV_no_singletons <- BMV_final
for (i in c("MEMO014","MEMO078","MEMO145","MEMO146","MEMO149","MEMO084","MEMO018","MEMO006",
"NEMO27","NEMO30","NEMO07","NEMO46","NEMO51","MEMO127","MEMO008")) {
BMV_no_singletons <- subset_samples(BMV_no_singletons, Sample != i)
}
BMV_no_singletons## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 45 taxa and 68 samples ]
## sample_data() Sample Data: [ 68 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 45 taxa by 7 taxonomic ranks ]#v.ord <- ordinate(BMV_no_singletons, method = "NMDS", k=2)
vegan_avgdist_no_single <- avgdist(as.data.frame(t(otu_table(BMV_no_singletons))),
sample=min_depth, iterations = 1000)
v.ord_no_single <- metaMDS(vegan_avgdist_no_single, k=2)## Run 0 stress 0.006869345
## Run 1 stress 0.008300676
## Run 2 stress 0.009725236
## Run 3 stress 0.004331806
## ... New best solution
## ... Procrustes: rmse 0.06446797 max resid 0.1129305
## Run 4 stress 0.0123011
## Run 5 stress 0.01555079
## Run 6 stress 0.007535474
## Run 7 stress 0.009235986
## Run 8 stress 0.01313614
## Run 9 stress 0.01246345
## Run 10 stress 0.009039595
## Run 11 stress 0.01091892
## Run 12 stress 0.008416282
## Run 13 stress 0.01561877
## Run 14 stress 0.008319308
## Run 15 stress 0.00795523
## Run 16 stress 0.008117063
## Run 17 stress 0.01464867
## Run 18 stress 0.006855152
## Run 19 stress 0.008680765
## Run 20 stress 0.01024769
## *** Best solution was not repeated -- monoMDS stopping criteria:
## 20: no. of iterations >= maxitnmds_ns <- plot_ordination(BMV_no_singletons, v.ord_no_single, type="samples", color="SKA_Subspecies", shape = "Municipality")+
scale_shape_manual(values = c(15,16,17,3:8), guide =guide_legend(label.theme = element_text(size=10)), name="Location")+
scale_color_viridis_d(begin=0, end =1, name="Mosquito species")+
#stat_ellipse(type = "norm", linetype = 2, aes_string(group="SKA_Subspecies"), show.legend = F)+
theme_bw()+
theme(panel.grid.minor = element_blank())+
guides(col = guide_legend(override.aes = list(shape = 15, size = 5),
label.theme = element_text(size=10, face="italic")))
nmds_ns
ggsave("figures/NMDS-eukaryotic-virome-no-singletons.pdf", nmds_ns, dpi=300)
v.ord <- ordinate(BMV_no_singletons, method = "PCoA")
pcoa_ns <- plot_ordination(BMV_no_singletons, v.ord, type="samples", color="SKA_Subspecies", shape = "Municipality")+
scale_shape_manual(values = c(15,16,17,3:8), guide =guide_legend(label.theme = element_text(size=10)), name="Location")+
scale_color_viridis_d(begin=0, end =1, name="Mosquito species")+
stat_ellipse(type = "norm", linetype = 2, aes_string(group="SKA_Subspecies"), show.legend = F)+
theme_bw()+
theme(panel.grid.minor = element_blank())+
guides(col = guide_legend(override.aes = list(shape = 15, size = 5),
label.theme = element_text(size=10, face="italic")))
pcoa_ns
Permanova test
samples_vegan_ns <- row.names(as.matrix(vegan_avgdist_no_single))
metadata_vegan_ns <- meta %>%
filter(Sample %in% samples_vegan_ns)
perm_ns <- adonis2(vegan_avgdist_no_single ~ SKA_Subspecies*Municipality,
data = metadata_vegan_ns)
perm_nspval_ns<-perm_ns$`Pr(>F)`[1]
Rsq_ns <- perm_ns$R2[1]
ord.legend <- cowplot::get_legend(pcoa)
alpha.legend <- cowplot::get_legend(alpha)
plots <- cowplot::align_plots(alpha+theme(legend.position = "none"),
pcoa+theme(legend.position = "none"), align = 'v', axis = 'l')
bottom<-cowplot::plot_grid(plots[[2]],
nmds+theme(legend.position = "none"), labels=c("B","C"))
cowplot::plot_grid(plots[[1]], alpha.legend, bottom, ord.legend, labels = c('A', ''), ncol=2, rel_widths = c(1, .3))
1.5 Relative abundance
1.5.1 Relative abundance per location
phylobar_location <- merge_samples(BMV_family, "Municipality")
phylobar_location_re.abund <- phylobar_location %>%
transform_sample_counts(function(x) {round(100*(x/sum(x)),5)} )
lc <- plot_relabund(phylobar_location_re.abund, fill = "Family") +
geom_bar(aes(fill=Family), stat="identity", position="stack")+
ylab("Relative abundance (%)")+
xlab("")+
theme_bw()+
theme(axis.text.x = element_text(angle=0, size = 10, vjust = 0.5, hjust = 0.5),
axis.text.y = element_text(size = 10),
axis.title.y = element_text(size = 12, vjust=0.5),
axis.title.x = element_text(size = 12,hjust = 0.5),
axis.ticks.y = element_blank(),
legend.text = element_text(face="italic"))+
scale_fill_manual(values = myFamCol, name="Viral family")+
scale_y_continuous(expand = c(0,0))+
scale_x_discrete(limits=rev(gsub("_","-",loc_order)))+
coord_flip()
Combine viral family barplot with mosquito species barplots from each location:
lc2<-ggarrange(lc+theme(legend.position = "none"), ggarrange(plotlist=plist, nrow=10, heights = c(0.2, rep(1,8),0.6)), ncol=2, widths = c(1,0.1))
ggsave("figures/viralfamily_barplot_loc.pdf", lc2, height = 6.92, width = 11.2, dpi=300)
1.5.2 Relative abundance per mosquito species
phylobar_species <- merge_samples(BMV_family, "SKA_Subspecies")
phylobar_species_re.abund <- phylobar_species %>%
transform_sample_counts(function(x) {round(100*(x/sum(x)),5)} )
sp <- plot_relabund(phylobar_species_re.abund, fill = "Family") +
geom_bar(aes(fill=Family), stat="identity", position="stack")+
ylab("Relative abundance (%)")+
xlab("")+
theme_bw()+
theme(axis.text.x = element_text(angle=0, size = 10, vjust = 0.5, hjust = 0.5, face="italic"),
axis.text.y = element_text(size = 10),
axis.title.y = element_text(size = 12, vjust=0.5),
axis.title.x = element_text(size = 12,hjust = 0.5),
axis.ticks.x = element_blank(),
legend.text = element_text(face="italic"),
legend.title = element_blank())+
scale_fill_manual(values = myFamCol, name="Viral family")+
scale_y_continuous(expand = c(0,0))+
#scale_x_discrete(guide = guide_axis(n.dodge=2))
scale_x_discrete(labels=c("Ae. japonicus", "Cx. p. molestus", "Cx. p. pipiens", "Cx. torrentium"))
ggsave("figures/viralfamily_barplot_species.pdf", sp , height = 6.92, width = 11.2, dpi=300)
Create combined legend for viral families and mosquito species:
sp2 <- plot_relabund(phylobar_species_re.abund, fill="Family", color="Sample")+
theme_bw()+
theme(legend.text = element_text(face="italic"),
legend.title = element_blank(),
legend.position = "top",
legend.box.just = "bottom")+
scale_fill_manual(values = myFamCol, name="a")+
scale_color_manual(values = myColors, name="b")+
guides(fill=guide_legend(nrow = 4),
col=guide_legend(override.aes =list(fill = myColors), direction = "vertical"))
sp_leg <- get_legend(sp2)
sp2
1.6 Eukaryotic virome heatmap
#p.fungi<-log10(pData(BMV_metaseq_species)$fungi_reads+1)
#p.fungi<-pData(BMV_metaseq_species)$fungi_reads
#p.fungi<-as.matrix(fungi2[fungi2$Sample %in% pData(BMV_metaseq_species)$Sample,2:5])
p.fungi<-log10(as.matrix(fungi2[fungi2$Sample %in% pData(BMV_metaseq_species)$Sample,2:5]))
p.fungi[p.fungi==-Inf]<-NA
colnames(p.fungi)<-as.data.frame(tax_table(BMV_fungi))$Phylum
left_ra=rowAnnotation('Family'=gcanno$Family,
"Blastx"= rowanno$blastx,
col=list('Family'=myFamCol,
"Blastx"=col_fun),
show_annotation_name=T,
annotation_name_side = "top",
annotation_name_gp = gpar(fontsize = 10, fontface = "bold"),
annotation_name_rot = 270,
annotation_legend_param=list("Blastx" = list(title ="Blastx % Identity",
direction="horizontal",
at=c(0,25,50,75,100)),
"Family"=list(title="Viral family", labels_gp = gpar(fontface="italic"))))
#Draw heatmap
column_ha = HeatmapAnnotation(Location=location,
'Mosquito species' = mosquito_species,
Fungi = anno_points(p.fungi, ylim = c(0, 6),
axis_param = list(side = "right"),
gp = gpar(fill = c(2:4,7), col = c(2:4,7)),
pch=1:length(colnames(p.fungi))),
show_annotation_name = T,
annotation_label = gt_render(c("Location", "Species", "log<sub>10</sub>(Fungi)")),
annotation_name_side = "right",
annotation_name_gp = gpar(fontsize = 10, fontface = "bold"),
annotation_name_rot = 0,
annotation_legend_param=list('Mosquito species'= list(title ='Mosquito species',
labels_gp = gpar(fontface="italic")),
'Location' = list(title='Location')),
col = list('Mosquito species'=c(myColors), Location=c(locColors)))
#n depends on the amount of taxa in 'BMV_metaseq_species'
hm <- plot_abundance(BMV_metaseq_species, n = n_species, log = T, norm = F, colclust = "bray",
col = heatmapCols, name = "Log2 Read Counts",
top_annotation=column_ha,
row_names_gp = gpar(fontsize = 8),
show_column_names = FALSE,
heatmap_legend_param = list(direction = "horizontal"),
cluster_rows = FALSE,
cluster_row_slices = FALSE,
row_split=factor(gcanno$GC,
levels = c("dsDNA","ssDNA","dsRNA","ssRNA(+)","ssRNA(-)","unknown")),
row_order=tax_gc[with(tax_gc, order(tax_gc$Genome.Composition, Family)),"Species"],
left_annotation=left_ra,
row_title_gp = gpar(fontsize=10),
row_title_rot=0,
border=T)Legends
lgd_list<-list(Legend(labels = colnames(p.fungi), title = "Fungi", type = "points", pch = 1:length(colnames(p.fungi)),
legend_gp = gpar(col = c(2:4,7)), background ="transparent"))draw(hm, heatmap_legend_side = "left", annotation_legend_side = "left",
merge_legend = T, legend_grouping="original",
annotation_legend_list = lgd_list)
2 RT-qPCR analysis
2.1 Prepare the data
Recalculate quantity based on mosquito dilutions:
qPCR <- qPCR %>%
mutate(Quantity = case_when(str_detect(Sample, "MEMO") ~ qPCR$"Quantity Mean" * 40,
str_detect(Sample, "NEMO") ~ qPCR$"Quantity Mean" * 100))Divide quantity of Xanthi chryso-like virus by 2 because it is a dsRNA virus:
qPCR <- qPCR %>%
mutate(Quantity = case_when(str_detect(Target, "XCV") ~ qPCR$Quantity/2,
TRUE ~ Quantity))Replace NA to 0:
Replace abbreviations for full names:
qPCR_noNA <- qPCR_noNA %>% mutate(Target, Target= case_when(Target=="CPV"~"Culex orthophasmavirus 2 (CPV)",
Target=="XCV"~"Xanthi chryso-like virus (XCV)",
Target=="WMV6"~"Wuhan Mosquito Virus 6 (WMV6)",
Target=="WMV4"~"Wuhan Mosquito Virus 4 (WMV4)",
Target=="Daeseongdong"~"Daeseongdong virus 2 (DV2)",
Target=="HMV4"~"Hubei mosquito virus 4 (HMV4)",
TRUE ~ Target))Merge metadata with qPCR data: Abrreviate locations to fit the names on the plots later on.
metadata <- meta %>% mutate(Municipality = case_when(Municipality == 'Frameries' ~ 'Fr',
Municipality == 'Dilsen-Stokkem' ~ 'DS',
Municipality == 'Kallo' ~ 'K',
TRUE ~ Municipality))
metaqPCR <- merge.data.frame(qPCR_noNA, metadata, by="Sample")Count number of tested samples per mosquito species:
metaqPCR %>%
select(Sample, SKA_Subspecies) %>%
distinct() %>%
group_by(SKA_Subspecies) %>%
summarise(n= n())2.2 Plot data
legendlabels <- c(expression(paste(italic("Aedes japonicus"), " (n=8)")),
expression(paste(italic("Culex pipiens molestus"), " (n=47)")),
expression(paste(italic("Culex pipiens pipiens"), " (n=127)")),
expression(paste(italic("Culex torrentium"), " (n=16)")))
qPCRviolin <- ggplot(metaqPCR, aes(x=SKA_Subspecies, y=Quantity))+
geom_violin(aes(fill=SKA_Subspecies, color=SKA_Subspecies), position="identity")+
geom_jitter(aes(color=SKA_Subspecies), size=1, width=0.1)+
theme_bw()+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
#panel.grid.major = element_blank(),
panel.grid.major.y = element_blank(),
legend.text.align = 0)+
scale_y_continuous(expand=expansion(mult = c(0, 0), add = c(0, 0.1)),
trans=scales::pseudo_log_trans(base = 10),
breaks = c(0, 10^seq(2,8,2)),
labels = scientific_10(c(0, 10^seq(2,8,2))))+
xlab(label = "")+
ylab(label = "Viral copies per mosquito")+
scale_fill_viridis_d(begin=0.4, end =0.9, name="", alpha = 0.5,
labels=legendlabels)+
scale_color_viridis_d(begin=0.4, end =0.9, name="",
labels=legendlabels)+
guides(fill = guide_legend(override.aes = list(alpha = 1)))+
facet_wrap(~Target, nrow=2, ncol=3)
ggsave("figures/qPCR-violin.pdf", dpi=300, width=11)
Plot qPCR overview per sample:
qPCRoverview <- ggplot(metaqPCR,
aes(x=Sample, y=Quantity))+
facet_nested(Target~Municipality,
space="free",
scales = "free_x",
labeller = labeller(Target = label_wrap_gen(10), Municipality=label_wrap_gen(5)))+
geom_col(aes(fill=SKA_Subspecies), position="identity", alpha=1)+
theme_bw()+
theme(axis.text.x = element_blank(),
panel.grid.major = element_blank())+
scale_y_continuous(expand=expansion(mult = c(0, 0), add = c(0, 0.1)),
trans = scales::pseudo_log_trans(base = 10),
breaks = c(0, 10^seq(2,8,2)),
labels = scientific_10(c(0, 10^seq(2,8,2))))+
xlab(label = "Sample")+
ylab(label = "Viral copies per mosquito")+
scale_fill_viridis_d(begin=0.4, end =0.9, name="")+
guides(fill = guide_legend(override.aes = list(alpha = 1),
label.theme = element_text(size=10, angle = 0, face = "italic")))
ggsave("figures/qPCR_overview.pdf", width = 18.7, height = 10.3, units = "in", dpi=300)
DS=Dilsen-Stokkem, Fr=Frameries, K=Kallo
DS=Dilsen-Stokkem, Fr=Frameries, K=Kallo
DS=Dilsen-Stokkem, Fr=Frameries, K=Kallo
DS=Dilsen-Stokkem, Fr=Frameries, K=Kallo
4 Phageome & Wolbachia bacteria
4.1 Coverage plot
phageblast <- read.table("data/phage_blast.tsv", header=F, sep="\t")
phageblast <- phageblast[phageblast$V4 > 2000,]
rRNA <- data.frame(name=c("16S", "23S", "5S"),
start=c(1136001, 1235938, 1238768),
end=c(1137446, 1238682, 1238878)
)
prophage <- data.frame(
name=c("p1", "p2", "p3", "p4", "p5"),
start=c(250652, 320006, 346157, 444716, 1376656),
end=c(266846, 337904, 360657, 483289, 1413160)
)
chunk_size <- 2500
MEMO_wolb <- read.table("data/depth_files/MEMO050.bowtie.depth", header = T)
#MEMO_wolb <- read.table("data/depth_files/MEMO043.bowtie.depth", header = T)
averages <- MEMO_wolb %>%
group_by(chunk = ceiling(row_number() / chunk_size)) %>%
summarize(across(starts_with("MEMO"), ~mean(.[!is.na(.)]), .names = "Average")) %>%
mutate(pos=chunk*chunk_size)
covplot <- ggplot(averages, aes(x = pos, y=Average))+
geom_area(color="black", fill="black", alpha=0.5)+
geom_rect(data = phageblast, inherit.aes=FALSE, aes(xmin=V9, xmax=V10, ymin=-max(averages$Average)*0.066, ymax=-max(averages$Average)*0.043,
fill="#3977AF", color="#3977AF"), alpha=1)+
geom_rect(data = rRNA, inherit.aes=FALSE, aes(xmin=start, xmax=end, ymin=-max(averages$Average)*0.036, ymax=-max(averages$Average)*0.012,
fill="red", color="red"), alpha=1)+
geom_rect(data = prophage, inherit.aes=FALSE, aes(xmin=start, xmax=end, ymin=-max(averages$Average)*0.036, ymax=-max(averages$Average)*0.012,
fill="limegreen", color="limegreen"), alpha=1)+
labs(x="Nucleotide position", y="Read depth")+
#ggtitle("Wolbachia coverage plot of sample MEMO050")+
scale_fill_identity(name = "",
breaks = c("#3977AF", "limegreen", "red"),
labels = c("phageWO prophage region (BLASTn)", "prophage regions (Genbank)", "rRNA genes"),
guide = "legend")+
scale_color_identity(name = "",
#breaks = c("#3977AF", "red"),
#labels = c("phageWO prophage region", "rRNA genes"),
guide = "none",#"legend"
)+
theme_bw()+
theme(legend.position = c(.2,.97),
legend.background=element_rect(fill=NA, color=NA),
legend.margin = margin(t = 0, r = 0, b = 0, l = 0, unit='cm'),
legend.key.size = unit(3, 'mm'),
plot.title = element_text(size=9, face="bold"),
#panel.grid.major = element_line(size = .25),
panel.grid=element_blank())+
guides(fill = guide_legend(override.aes = list(alpha = 1)))
covplot
#ggsave("figures/wolbachia_coverageplot_MEMO043.pdf", dpi=300, width = 7.5, height=5)
# Define the function
process_file <- function(file_path, sample) {
MEMO_wolb <- read.table(file_path, header = TRUE)
averages <- MEMO_wolb %>%
group_by(chunk = ceiling(row_number() / chunk_size)) %>%
summarize(across(starts_with(c("MEMO", "NEMO")), ~mean(.[!is.na(.)]), .names = "Average")) %>%
mutate(pos = chunk * chunk_size)
max_y <- max(averages$Average)
if (max_y > 200) {
max_y <- 200
}
if (sample == "MEMO100" | sample == "MEMO139"){
max_y <- 15
}
covplot <- ggplot(averages, aes(x = pos, y = Average)) +
geom_area(color = "black", fill = "black", alpha = 0.5) +
geom_rect(data = phageblast, inherit.aes = FALSE, aes(
xmin = V9, xmax = V10,
ymin = -max_y * 0.066, ymax = -max_y * 0.043,
fill = "#3977AF", color = "#3977AF"
), alpha = 1) +
geom_rect(data = rRNA, inherit.aes = FALSE, aes(
xmin = start, xmax = end,
ymin = -max_y * 0.036, ymax = -max_y * 0.012,
fill = "red", color = "red"
), alpha = 1) +
geom_rect(data = prophage, inherit.aes = FALSE, aes(
xmin = start, xmax = end,
ymin = -max_y * 0.036, ymax = -max_y * 0.012,
fill = "limegreen", color = "limegreen"
), alpha = 1) +
labs(x = "Nucleotide position", y = "Read depth", title=sample) +
scale_fill_identity(
name = "",
breaks = c("#3977AF", "limegreen", "red"),
labels = c("phageWO prophage region (BLASTn)", "prophage regions (Genbank)", "rRNA genes"),
guide = "legend"
) +
scale_color_identity(
name = "",
guide = "none"
) +
theme_bw() +
theme(
legend.position = c(.15, .97),
legend.background = element_rect(fill = NA, color = NA),
legend.margin = margin(t = 0, r = 0, b = 0, l = 0, unit = 'cm'),
legend.key.size = unit(3, 'mm'),
plot.title = element_text(size = 9, face = "bold"),
panel.grid = element_blank()
) +
guides(fill = guide_legend(override.aes = list(alpha = 1)))
covplot <- covplot + coord_cartesian(ylim = c(NA, max_y))
return(covplot)
}
samples <- c('MEMO005', 'MEMO078', 'NEMO46', 'MEMO141', 'NEMO42', 'MEMO085', 'MEMO073', 'NEMO35', 'MEMO108',
'MEMO139', 'MEMO057', 'MEMO056', 'MEMO105', 'MEMO129',
'MEMO100', 'MEMO043')
# Apply the function to each file path using map
covplots <- purrr::map2(paste0("data/depth_files/", samples, ".bowtie.depth"), samples, process_file)
p <- ggarrange(plotlist = covplots, ncol = 4, nrow=4, common.legend=T)
p
ggsave(file="figures/wolbachia_coverageplots.pdf", plot=p, dpi=300, height=10, width=15)
#covplot <- ggplot(MEMO_wolb, aes_string(x=pos, y=names(MEMO_wolb[3])))+
# geom_line()+
# geom_rect(data = phageblast, inherit.aes=FALSE, aes(xmin=V9, xmax=V10, ymin=-max(MEMO_wolb[3])*0.066, ymax=-max(MEMO_wolb[3])*0.043,
# fill="#3977AF", color="#3977AF"), alpha=1)+
# geom_rect(data = rRNA, inherit.aes=FALSE, aes(xmin=start, xmax=end, ymin=-max(MEMO_wolb[3])*0.036, ymax=-max(MEMO_wolb[3])*0.012,
# fill="red", color="red"), alpha=1)+
# geom_rect(data = prophage, inherit.aes=FALSE, aes(xmin=start, xmax=end, ymin=-max(MEMO_wolb[3])*0.036, ymax=-max(MEMO_wolb[3])*0.012,
# fill="limegreen", color="limegreen"), alpha=1)+
# labs(x="Nucleotide position", y="Read depth")+
# #ggtitle("Wolbachia coverage plot of sample MEMO050")+
# scale_fill_identity(name = "",
# breaks = c("#3977AF", "limegreen", "red"),
# labels = c("phageWO prophage region (BLASTn)", "prophage regions (Genbank)", "rRNA genes"),
# guide = "legend")+
# scale_color_identity(name = "",
# #breaks = c("#3977AF", "red"),
# #labels = c("phageWO prophage region", "rRNA genes"),
# guide = "none",#"legend"
# )+
# theme_bw()+
# theme(legend.position = c(.3,.93), #"top"
# legend.background=element_rect(fill=NA, color=NA),
# legend.margin = margin(t = 0, r = 0, b = 0, l = 0, unit='cm'),
# legend.key.size = unit(3, 'mm'),
# plot.title = element_text(size=9, face="bold"),
# #panel.grid.major = element_line(size = .25),
# panel.grid=element_blank())+
# guides(fill = guide_legend(override.aes = list(alpha = 1)))
#
#covplot4.2 Swarmplot
library(ggbeeswarm)
#phage <- read.table("data/wolbachia_phage_ratio.tsv", sep="\t", header = T)
#phage <- read.table("data/wolbachia_phage_bowtieratio_no_rRNA.tsv", sep="\t", header = T)
phage <- read.table("data/wolbachia_prophage_gb_bowtieratio_no_rRNA.tsv", sep="\t", header = T)
phage <- merge(phage, meta, by="Sample")
phage <- phage %>%
mutate(ratio=case_when(ratio==NA ~ 0,
nonphage<1 ~ 0,
TRUE ~ ratio))
phage %>%
filter(ratio>0) %>%
summarise(median(ratio))phage <- phage %>%
mutate(color=case_when(ratio==0 ~ "NA",
TRUE ~ color))
swarmplot <- ggplot(phage, aes(x = SKA_Subspecies, y= ratio, color=color))+
geom_quasirandom(width = .4)+
scale_color_manual(values = c("<3.5"="#3977AF", ">3.5"="darkorange", "NA"="#7F7F7F"),
name="",
labels=c("Prophage", "Real phage particle?", "Average depth < 1"))+
geom_text(aes(label=ifelse(ratio > 3.5&nonphage>1, Sample,"")), nudge_x=.5, size=3,
show.legend = F)+
ylab(paste0("<span style='font-size: 11pt'>Depth ratio</span><br><span style='font-size: 8pt'>*(average depth phage/average depth nonphage)*</span>"))+
theme_bw()+
theme(axis.title.x = element_blank(),
axis.text.x = element_text(face="italic", size = 6),
legend.position = c(.2, .93),
legend.background = element_blank(),
legend.key.size = unit(3, 'mm'),
panel.grid = element_blank(),
axis.title.y = ggtext::element_markdown())
swarmplot
4.3 Wolbachia
Wolbachia bwa-mem2 mapped
wMel <- read.table("data/wMel.txt", header=F, dec=".", sep=" ")
wPip <- read.table("data/wPip.txt", header=F, dec=".", sep=" ")
phageWO <- read.table("data/phageWO.txt", header=F, dec=".", sep=" ")
colnames(wMel) <- c("Sample", "numreads", "covbases", "coverage", "meandepth")
colnames(wPip) <- c("Sample", "numreads", "covbases", "coverage", "meandepth")
colnames(phageWO) <- c("Sample", "supergroup", "numreads", "covbases", "coverage", "meandepth")
wPip$supergroup <- "wPip"
wMel$supergroup <- "wMel"
wolb <- rbind(wMel, wPip, phageWO)
wolb %<>%
plyr::mutate(supergroup=str_replace(string=supergroup, pattern = "NODE_12_length_11674_cov_94.907217_MEMO129", replacement = "phageWO2")) %>%
plyr::mutate(supergroup=str_replace(string=supergroup, pattern = "NODE_1_length_31159_cov_25.624799_MEMO050", replacement = "phageWO1"))
wolb$numreads[wolb$coverage<5] <- 0
wolb$meandepth[wolb$coverage<5] <- 0
left_join(wolb, meta) %>%
filter(supergroup=="wPip") %>%
group_by(SKA_Subspecies) %>%
summarise(n=n(), wol_pos=sum(numreads>0)) %>%
mutate(wol_freq=round(wol_pos/n*100, 3))wviolin <- pivot_wider(wolb, id_cols = "Sample", names_from = "supergroup", values_from = "numreads") %>%
ggplot(aes(x = meta[meta$Sample!="NEMO39",]$SKA_Subspecies, y=wPip))+
geom_boxplot(aes(color=meta[meta$Sample!="NEMO39",]$SKA_Subspecies), width=0.1, outlier.shape = NA)+
geom_violin(aes(fill=meta[meta$Sample!="NEMO39",]$SKA_Subspecies), color=NA, show.legend = F, trim = T)+
geom_jitter(aes(color=meta[meta$Sample!="NEMO39",]$SKA_Subspecies), size=1, alpha=0.6,
position=position_jitter(w=0.1,h=0)) +
theme_bw()+
theme(axis.text.x = element_blank(),
panel.grid = element_blank(),
axis.ticks.x = element_blank())+
xlab(label = "")+
ylab(label = "Wolbachia read count")+
scale_fill_manual(values=alpha(myColors, 0.1), name='')+
scale_color_viridis_d(begin=0.4, end =0.9, name="")+
guides(fill = guide_legend(#override.aes = list(alpha = 1),
label.theme = element_text(size=8, angle = 0, face = "italic")))+
scale_y_continuous(trans=scales::pseudo_log_trans(base = 10), breaks = c(0, 10^seq(2,8,2)))
wviolin
ggsave("figures/wolbachia_readcount.pdf", dpi=300)
ggarrange(ggarrange(covplot+theme(legend.key.size = unit(3, 'mm')), swarmplot,
ncol=1, labels = "AUTO", align="v"),
wviolin+
theme(legend.position = c(.15, .92),
legend.background = element_blank()),
labels = c("", "C"), widths = c(1, 1.25), align = "v")
ggsave("figures/combined_wolbachia.pdf", dpi=300, width = 12)
p1<-ggarrange(covplot+theme(legend.key.size = unit(3, 'mm')), swarmplot,
ncol=1, labels = "AUTO")
p2 <- wviolin+
theme(legend.position = c(.15,.93),
legend.background = element_blank(),
panel.background = element_blank())
p2
library(patchwork)
patch <- p2 + plot_spacer() + covplot + swarmplot + plot_layout(widths = c(1.5, .01, 1),
design = "
123
124")
patch + plot_annotation(tag_levels = "A") &
theme(plot.tag = element_text(face = "bold"))
Second layout
patch2 <- covplot+theme(legend.position = c(0.3,0.97)) + swarmplot + plot_spacer() + p2 + plot_layout(widths = c(1, .01, 1.5),
design = "
134
234")
patch2 + plot_annotation(tag_levels = "A") &
theme(plot.tag = element_text(face = "bold"))
barplot
wb <- pivot_wider(wolb, id_cols = "Sample", names_from = "supergroup", values_from = "numreads") %>%
ggplot(aes(x = Sample, y=wPip))+
geom_col(aes(fill=meta[meta$Sample!="NEMO39",]$SKA_Subspecies))+
theme_bw()+
theme(axis.text.x = element_blank(),
panel.grid.major = element_blank())+
scale_y_continuous(expand=expansion(mult = c(0, 0.01), add = c(0, 0.1)))+
xlab(label = "Sample")+
ylab(label = "Wolbachia read count")+
labs(shape="Virus", color="Virus")+
scale_fill_viridis_d(begin=0.4, end =0.9, name="")+
guides(fill = guide_legend(override.aes = list(alpha = 1),
label.theme = element_text(size=10, angle = 0, face = "italic")))+
facet_grid(~meta[meta$Sample!="NEMO39",]$Municipality, scales = "free", space = "free")
wb
4.4 Phageome
phageotu<-read.table("data/BEmosq_classification-85-taxfile-1000nt.tsv", sep="\t", header = T)
phage<-phageotu[phageotu$Phylum == "Phage",]
rownames(phage)<- phage$Contig
phage <- subset(phage, select=-Contig)
newtax<-tax[!rownames(tax) %in% rownames(phage),]
newtax <- rbind(newtax, phage)
tax_table(BMV) <- as.matrix(newtax) #Maybe better solution
#newtax.UF <- tax_table(as.matrix(newtax))
#BMV <- phyloseq(OTU.UF, newtax.UF, meta.UF)
BMV.V <- subset_taxa(BMV, Kingdom=="Viruses")
phageome <- subset_taxa(BMV.V, Phylum=="Phage" | Order=="Caudovirales" | Family=="Inoviridae")
phageome## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 41 taxa and 197 samples ]
## sample_data() Sample Data: [ 197 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 41 taxa by 7 taxonomic ranks ]otu_table(phageome) <- as.data.frame(otu_table(phageome)) %>%
mutate(otu=rownames(.)) %>%
pivot_longer(cols=1:197, names_to = "Sample", values_to = "abundance") %>%
mutate(abundance=if_else(otu %in% c("NODE_1_length_31159_cov_25.624799_MEMO050|full",
"NODE_12_length_11674_cov_94.907217_MEMO129|full")
& Sample != "NEMO039", 0, as.double(abundance))) %>%
pivot_wider(names_from = Sample, values_from = abundance) %>%
column_to_rownames('otu') %>%
otu_table(taxa_are_rows = T)
#sample_data(phageome) <- data.frame(wv.meta, row.names = 1)
#sample_data(phageome)
phage_pruned <- prune_samples(sample_sums(phageome)>0, phageome)
phage_pruned <- prune_taxa(taxa_sums(phage_pruned)>0, phage_pruned)
phage_pruned## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 28 taxa and 4 samples ]
## sample_data() Sample Data: [ 4 samples by 13 sample variables ]
## tax_table() Taxonomy Table: [ 28 taxa by 7 taxonomic ranks ]pp <- as.data.frame(otu_table(phage_pruned))
p.merge <- merge_taxa(phage_pruned, 1:2, 2)
p.merge <- merge_taxa(p.merge, 2:24, 2)
p.merge <- merge_taxa(p.merge, 3:4, 2)
tax_table(p.merge)## Taxonomy Table: [4 taxa by 7 taxonomic ranks]:
## Kingdom Phylum
## NODE_4_length_5754_cov_10.923727_MEMO048|full "Viruses" NA
## NODE_2_length_9043_cov_21.187821_MEMO141|full "Viruses" NA
## NODE_17_length_1194_cov_3.745748_MEMO017|full "Viruses" "Uroviricota"
## NODE_1_length_5000_cov_44.805403_NEMO08|full "Viruses" "Phage"
## Class Order
## NODE_4_length_5754_cov_10.923727_MEMO048|full NA NA
## NODE_2_length_9043_cov_21.187821_MEMO141|full NA NA
## NODE_17_length_1194_cov_3.745748_MEMO017|full "Caudoviricetes" "Caudovirales"
## NODE_1_length_5000_cov_44.805403_NEMO08|full "unclassified" "unclassified"
## Family Genus
## NODE_4_length_5754_cov_10.923727_MEMO048|full NA NA
## NODE_2_length_9043_cov_21.187821_MEMO141|full NA NA
## NODE_17_length_1194_cov_3.745748_MEMO017|full "Siphoviridae" "unclassified"
## NODE_1_length_5000_cov_44.805403_NEMO08|full "unclassified" "unclassified"
## Species
## NODE_4_length_5754_cov_10.923727_MEMO048|full NA
## NODE_2_length_9043_cov_21.187821_MEMO141|full NA
## NODE_17_length_1194_cov_3.745748_MEMO017|full "Erwinia phage Midgardsormr38"
## NODE_1_length_5000_cov_44.805403_NEMO08|full "unclassified"## OTU Table: [4 taxa and 4 samples]
## taxa are rows
## MEMO017 MEMO048 MEMO141 NEMO08
## NODE_4_length_5754_cov_10.923727_MEMO048|full 0 3945 0 0
## NODE_2_length_9043_cov_21.187821_MEMO141|full 0 0 17529 0
## NODE_17_length_1194_cov_3.745748_MEMO017|full 561 0 0 0
## NODE_1_length_5000_cov_44.805403_NEMO08|full 0 0 0 7751plot_heatmap(p.merge, taxa.label = "Species", low ="#FFF7B8", high = "#800026", na.value = "#FFFFCC")+
theme_bw()+
theme(axis.title = element_blank())
phage_tax <- as.data.frame(tax_table(p.merge))
phage_tax <- replace_na(phage_tax, list(Phylum="Uroviricota", Class="Caudoviricetes", Order="Caudovirales",
Family="unclassified", Genus="unclassified", Species="unclassified"))
tax_table(p.merge) <- as.matrix(phage_tax)4.4.1 Wolbachia vs virus
viral_reads <- as.data.frame(colSums(otu_table(BMV.V2)))
viral_reads <- rownames_to_column(viral_reads, "Sample")
colnames(viral_reads) <- c("Sample", "viral_reads")
wol_virus_df <- left_join(wPip, viral_reads)
fit<-lm(log10(viral_reads+1)~coverage, data=wol_virus_df)
summary(fit)##
## Call:
## lm(formula = log10(viral_reads + 1) ~ coverage, data = wol_virus_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.788 -1.684 -1.542 1.948 4.718
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.788536 0.244070 7.328 6.05e-12 ***
## coverage -0.003115 0.004725 -0.659 0.51
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.117 on 195 degrees of freedom
## Multiple R-squared: 0.002225, Adjusted R-squared: -0.002892
## F-statistic: 0.4348 on 1 and 195 DF, p-value: 0.5104wol_virus_df %>%
left_join(meta[meta$Sample!="NEMO39",]) %>%
#mutate(viral_reads=viral_reads+1) %>%
ggplot(aes(x=coverage, y=viral_reads))+
geom_point(aes(col=SKA_Subspecies))+
geom_smooth(method=lm)+
geom_vline(xintercept = 5, linetype="dashed",
color = "red")+
#scale_x_continuous(trans='log10')+
scale_y_continuous(trans=scales::pseudo_log_trans(base = 10), breaks = c(0, 10^seq(2,8,2)))+
#scale_y_continuous(trans='log10')+
scale_color_manual(values = myColors)+
ylab("log10(viral reads+1)")+
xlab("Wolbachia wPip genome coverage")+
labs(color="Mosquito species",
title = paste("Adj R2 = ",signif(summary(fit)$adj.r.squared, 5),
"Intercept =",signif(fit$coef[[1]],5 ),
" Slope =",signif(fit$coef[[2]], 5),
" P =",signif(summary(fit)$coef[2,4], 5)))+
theme_bw()+
guides(col = guide_legend(override.aes = list(shape = 15, size = 5),
label.theme = element_text(size=10, face="italic")))
ggsave("figures/wolbachia_virus_correlation.pdf", dpi = 300)
left_join(wPip, viral_reads) %>%
ggplot(aes(x=coverage>5, y=viral_reads))+
scale_y_continuous(trans=scales::pseudo_log_trans(base = 10), breaks = c(0, 10^seq(2,8,2)))+
geom_boxplot()+
geom_point()
left_join(wPip, rownames_to_column(vcount, "Sample")) %>%
ggplot(aes(x=coverage, y=viral_count))+
#geom_boxplot()+
geom_point()+
geom_smooth(method = lm)
4.4.2 pWCP coverage
pWCP_cov <- read.table("data/pWCP_stats.tsv", header=T)
#names(pWCP_cov) <- c("Sample", "pWCP_cov")
left_join(wPip, pWCP_cov, by="Sample") %>%
left_join(meta[meta$Sample!="NEMO39",]) %>%
mutate(meandepth.x=case_when(coverage.x<5 ~ 0,
TRUE ~ meandepth.x),
meandepth.y=case_when(coverage.x<5 ~ 0,
TRUE ~ meandepth.y),
completeness=cut(pWCP_cov$coverage, breaks = c(0, 25, 50, 75, 100), include.lowest = T)) %>%
ggplot(aes(x=meandepth.x, y=meandepth.y))+
geom_smooth(method = "lm", se=F, col="lightgrey")+
geom_point(aes(col=SKA_Subspecies, shape=completeness))+
scale_color_manual(values = myColors)+
scale_shape_manual(values=c(3, 17, 15, 19))+
scale_y_continuous(trans = pseudo_log_trans())+
scale_x_continuous(trans = pseudo_log_trans())+
labs(color="Mosquito species",
x="Wolbachia sequencing depth",
y="pWCP plasmid sequencing depth")+
theme_bw()+
guides(col = guide_legend(override.aes = list(shape = 15, size = 5),
label.theme = element_text(size=10, face="italic")))
ggsave("figures/pWCP_coverage.pdf", dpi=300)
left_join(wPip, pWCP_cov, by="Sample") %>%
left_join(meta[meta$Sample!="NEMO39",]) %>%
mutate(meandepth.x=case_when(coverage.x<5 ~ 0,
TRUE ~ meandepth.x),
meandepth.y=case_when(coverage.x<5 ~ 0,
TRUE ~ meandepth.y),
completeness=cut(pWCP_cov$coverage, breaks = c(0, 25, 50, 75, 100), include.lowest = T)) %>%
ggplot(aes(x=meandepth.x, y=meandepth.y))+
geom_smooth(method = "lm", se=F, col="lightgrey")+
geom_point(aes(col=coverage.y, shape=SKA_Subspecies))+
scale_color_gradientn(colours = viridis(10), guide = guide_colourbar(direction = "horizontal", title.position="top"))+
scale_y_continuous(trans = pseudo_log_trans())+
scale_x_continuous(trans = pseudo_log_trans())+
labs(color="pWCP completeness (%)",
shape="Mosquito species",
x="Wolbachia sequencing depth",
y="pWCP plasmid sequencing depth")+
theme_bw()